In speech recognition, what kind of signal is used?
Not sure I understand what you mean by signal. However, incoming audio must be cleaned and processed for a machine to understand. Some people speak faster and others slower. First thing is to sample in the incoming audio. In other words use analog electic signals and store them as binary values at a set rate of sampling frequency.

See Nyquist Shannon theorem.

Now that it is sampled we need to clean it up and extract only the voice data. Effectively the sample is a set of values (frequencies) recorded over time. Or a signal :).
We can cut up this signal into many smaller components of a few milliseconds say 10ms. Each of the millisecond samples is further processed 
A Fourier transform is generally used to break a signal into components, or frequencies that make up the signal. This is basic signal processing and we have not even entered machine learning yet.

Now we have a fingerprint of each 10ms sample these samples are then fed into a neural network. Samples are broken between frequency drops to create blocks of sound which may or may not be a word. These blocks are passed to the neural network.

A traditional feed forward neural network will not work as it forgets the data. So a recurrent neural network is used.
To train the neural network known words are tagged and encoded as a number. The neural network will output a number and the difference between this number and the expected number is the error. The error is then used to update the weights

Once we have the words, those can be further processed by other networks that specialise in natural language understanding